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Here is the latest AI information for August 16.

Learning Tutorials

1. NVIDIA has released a comprehensive tutorial.

It introduces how to shrink various large language models by combining weight pruning and knowledge distillation techniques, without the need to train new models from scratch.

The tutorial uses the example of converting the Llama-3.1 8B model to the smaller Llama-3.1-Minitron 4B model.

Tutorial link: https://developer.nvidia.com/blog/how-to-prune-and-distill-llama-3-1-8b-to-an-nvidia-llama-3-1-minitron-4b-model/

Open Source Projects

1. An open-source and powerful video multilingual AI dubbing/translation tool: Linly-Dubbing.

It can automatically download videos from all platforms with one click, translate video subtitles, separate human voices from the original video, clone voice tones and re-dub, then synthesize the video, all automated.

GitHub: https://github.com/Kedreamix/Linly-Dubbing

It has the following features:

• Multilingual Support: Supports dubbing and subtitle translation in Chinese and many other languages, meeting internationalization needs.
• AI Intelligent Speech Recognition: Uses WhisperX/FunASR for speech recognition, providing accurate speech-to-text conversion and speaker recognition.
• Large Language Model Translation: Uses large language models (such as GPT) for fast and accurate translation, ensuring professionalism and naturalness.
• AI Voice Cloning: Uses GPT-SoVITS to clone the original video dubbing, generating highly similar voices while maintaining consistency in emotion and tone.
• Digital Human Lip-syncing Technology: Through lip-syncing technology Linly-Talker, makes dubbing highly compatible with video images, enhancing realism and interactivity.
• Automatic Video Download: Utilizes the powerful open-source command-line tool yt-dlp to automatically download videos from almost all platforms with one click.

2. A comprehensive learning collection of Retrieval-Augmented Generation (RAG) techniques on GitHub: RAG_Techniques.

It covers 22 RAG techniques from basic to advanced, and provides detailed documentation, implementation guides, and sample code for each technique, helping everyone better understand and apply them.

GitHub: https://github.com/NirDiamant/RAG_Techniques

It includes context enrichment techniques, multi-faceted filtering, fusion retrieval, semantic chunking, knowledge graph integration, multimodal retrieval, and more. It's very comprehensive!